Biography

He is a Professor in the Department of Industrial and System Engineering at University of Washington. He obtained B.S. degree in Industrial Engineering from Seoul National University in 2001. He received Ph.D. degree in Industrial Engineering from Texas A&M University, College Station, TX in 2011 under the guidance of Yu Ding in Industrial and Systems Engineering and Jianhua Huang in Statistics at Texas A&M University. Before joining the University of Washington in 2024, he was a Professor in the Department of Industrial and Manufacturing Engineering at Florida State University.

His main research interest lies in machine learning and data science with applications to advanced manufacturing and physical science. He is particularly interested in modeling and analysis of object data. It concerns statistical analysis of complex objects and their visual features (such as image, shape, motion, function and directions). Object data are normally non-Euclidean features. Conventional statistical tools developed for Euclidean data do not apply here. Relevant research around object data is to define proper probability spaces of object data and develop associated statistical inference algorithms. Many of his methodological studies are motivated by the problem of understanding processing-structure-property relations in manufacturing and physical sciences. In 2021, he authored the book Data Science for Nano Image Analysis, which summarizes many of his works in these areas. Another application is Data Science for Motion and Time Analysis in Operations Research.

His more recent research is surrogate modeling of physical and computer experiments. He applies the surrogate works for creating digital twins in cyber-physical systems and developing AI-driven scientific discovery platforms. Gaussian processes have been the canonical choice for surrogate modeling of physical and computer experiments. However, they are not great modeling choices for nonstationarity, regime changes, and discontinuity, which are prevailing in many of his application systems. He has recently started working on some alternative surrogates such as Jump Gaussian Process. He has recent works in optimizing experiments or data acquisition processes to learn the new surrogate, i.e., Active Learning of Jump Gaussian Process Surrogates.

Education

• Ph.D. 2011. Texas A&M University. Industrial & Systems Engineering
• B.S. 2001. Seoul National University. Industrial Engineering

Previous appointments

• Professor, Industrial and Manufacturing Engineering, Florida State University (2023)
• Associate Professor, Industrial and Manufacturing Engineering, Florida State University (2017-2023)
• Assistant Professor, Industrial and Manufacturing Engineering, Florida State University (2011-2017)

Research Statement

My research areas are data science and machine learning with applications to advanced manufacturing, operations research, and physical/health sciences. One of my major focus areas is on object data analysis, which concerns a statistical analysis of populations of complex objects such as images, shapes, motions, functions, and directions. These find many interesting applications in operations research, materials imaging, and smart manufacturing. Many of these methodological studies are motivated by different domain problems:  human motion and time study in operations research, understanding the relationship between material structures and properties in pharmaceutical/biological/materials science, and investigating the process-structure relation in smart manufacturing. My other research focuses on surrogate modeling of physical or computer experiments and optimizing data acquisition strategies (a.k.a. active ML) to explore large experimental spaces effectively. The main application of this research is the development of an autonomous system for smart factory and materials manufacturing. 

Current projects

Data-Driven Adaptive Control of Shape Evolution with Regime Changes

Air Force Office of Scientific Research (AFOSR) Grant No. FA9550-23-1-0673 Dynamical Systems and Control Theory Program. 

CDS&E/Collaborative Research: Local Gaussian Process Approaches for Predicting Jump Behaviors of Engineering Systems

National Science Foundation (NSF) Computational and Data-Enabled Science and Engineering Program. Grant No. 2152655/2152679

New Data Science for Human Operational Analysis in Smart Manufacturing

National Science Foundation (NSF) Operations Engineering Program. Grant No. 2132311

Select publications

1. Park, C. (2022) Jump Gaussian Process Model for Estimating Piecewise Continuous Regression Functions. Journal of Machine Learning Research. 23(278):1−37.
2. Park, C., Noh, S., & Srivastava, A. (2022). Data Science for Motion and Time Analysis with Modern Motion Sensor Data. Operations Research. 70(6):3217-3233
3. Park, C. and Ding, Y. (2021) Data Science for Nano Image Analysis. Springer Nature. ISBN 978-3-030-72821-2
4. Park, C., & Apley, D. (2018) Patchwork Kriging for Large-scale Gaussian Process Regression. Journal of Machine Learning Research. 19(7): 1-43
5. Park, C., Woehl, T. J., Evans, J. E., & Browning, N. D. (2015). Minimum Cost Multi-way Data Association for Optimizing Multitarget Tracking of Interacting Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 611-624
6. Park, C. (2014). Estimating Multiple Pathways of Object Growth using Nonlongitudinal Image Data. Technometrics, 56(2), 186-199
7. Park, C., Huang, J. Z., Ji, J., & Ding, Y. (2013). Segmenting, Inference and Classification of Partially Overlapping Nanoparticles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 669-681
8. Park, C., Huang, J. Z., & Ding, Y. (2010). A Computable Plug-in Estimator of Minimum Volume Sets for Novelty Detection. Operations Research, 58(5), 1469-1480

Honors & awards

• BrainPool Faculty Fellowship, National Research Foundation - Korea (2020)
• Ralph E. Powe Jr. Faculty Enhancement Award, Oak Ridge Associated Universities (2013)
• Senior Member, Institute of Industrial and Systems Engineers (IISE)
• Senior Member, Institute of Electrical and Electronics Engineers (IEEE)
• Member, Institute for Operations Research and the Management Sciences (INFORMS)